Fitting multilevel multivariate models with missing data in responses and covariates that may include interactions and non‐linear terms

104 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A Comparison of Three-Step Approaches for Auxiliary Variables in Latent Class and Latent Profile Analysis (2017, methodology): “There has been a comparison of the PC method to a Bayesian approach in Goldstein, Carpenter, and Browne (2014) to multiply impute missingness in nested data.”
Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model (2012, unknown): “A new joint modelling approach for handling interactions and non-linearities has recently been proposed by Goldstein et al. 18”
Multiple Imputation of Missing Data for Multilevel Models (2018, methodology): “As an alternative, the model-based treatment of missing data can be implemented in a Bayesian analysis approach (Erler et al., 2016; see also Goldstein et al., 2014; Zhang & Wang, 2016).”